Article

Audit to Forget: A Unified Method to Revoke Patients’ Private Data in Intelligent Healthcare

2023-02-21

Abstract excerpt

Revoking personal private data is one of the basic human rights, which has already been sheltered by several privacy-preserving laws in many countries. However, with the development of data science, machine learning and deep learning techniques, this right is usually neglected or violated as more and more patients’ data are being collected and used for model training, especially in intelligent healthcare, thus mak...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 4a7c2e6a-e660-5f84-96c3-108a2c1a8c82
DOI: 10.1101/2023.02.17.529040

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Audit to Forget: A Unified Method to Revoke Patients’ Private Data in Intelligent HealthcareDOI 10.1101/2023.02.17.529040Read

Machine Unlearning-Enabled Cyber-Resilient AIoT Framework for Privacy-Preserving and Adaptive Smart Pharmaceutics in Personalized Drug Delivery SystemsSemantic proximity Read

Automated Detection of Poor-Quality Data: Case Studies in HealthcareSemantic proximity Read

<em>ppAIsec</em>: Privacy-Preserving Artificial Intelligence Models in Healthcare Security—A Synthesis of AI FrameworksSemantic proximity Read

Privacy-Preserving Machine Learning (PPML)Inference for Clinically Actionable Models: How to Monetize ESSG Modelling Efforts?Semantic proximity Read

Machine Unlearning for GDPR Right-to-Erasure in Antimicrobial Resistance Prediction ModelsSemantic proximity Read

<em>safeMEDInet</em>: Federated AI Systems for Security and Privacy-Preserving Threat Detection in Distributed HealthcareSemantic proximity Read

Privacy-first health research with federated learningSemantic proximity Read

A Blockchain-Based Federated Learning Approach with Secure Third-Party Computation System for Securing Electronic Health RecordsSemantic proximity Read

A Japan-Calibrated Synthetic Medical Claims Benchmark for Anti-Fraud Machine LearningSemantic proximity Read

SynTrustBench: An Evidence-Gated and Executable Benchmark for Trustworthiness Claims in Synthetic Clinical DataSemantic proximity Read

Clinical MLOps: A Framework for Responsible Deployment and Observability of AI Systems in Cloud-Native Healthcare PlatformsSemantic proximity Read

Adaptive Trust-Driven Federated Learning with Blockchain for Secure AI Healthcare DiagnosticsSemantic proximity Read